class_docstr: MNIST Dataset.
class_docstr_tok: ["MNIST","Dataset","."]
code: def __init__(self, root=_DATA, train=True, transform=None):
      train_images, train_labels, test_images, test_labels = mnist_raw(
          root=root
      )
  
      if train:
          self.images = train_images
          self.labels = torch.from_numpy(train_labels).long()
      else:
          self.images = test_images
          self.labels = torch.from_numpy(test_labels).long()
  
      self.transform = transform
  
file_path: cleverhans/tutorials/torch/datasets.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import array
  import gzip
  import os
  from os import path
  import struct
  from six.moves.urllib.request import urlretrieve
  import numpy
  import torch
label_desc: Use image node_id 1 to create a new MNISTDataset object from inherited base classes: torch.utils.data with example: obj = MNISTDataset(root, train, transform)